Guidance Index for Shallow Landslide Hazard Analysis

Rainfall-induced landslides are one of the most frequent hazards on slanted terrains. They lead to considerable economic losses and fatalities worldwide. Intense storms with high-intensity and long-duration rainfall have high potential to trigger rapidly moving soil masses due to changes in pore water pressure and seepage forces. Nevertheless, regardless of the intensity-duration of the rainfall, shallow landslides are influenced by antecedent soil moisture conditions. To the present day, no system exists that dynamically interrelates these two factors. This work establishes a relationship between antecedent soil moisture and rainfall expressed in the form of a Shallow Landslide Index (SLI) at 1km2 resolution for the United States. The proposed mathematical model is based on a logistic regression-learning algorithm that systematically adapts from previous landslide events listed in a comprehensive landslide inventory. Because landslides are considered to be the product of the interaction of static and dynamic factors, static factors are examined first. Also, because significant uncertainties are found when mapping factors in large spatial scales, buffer and threshold techniques are used to downscale areas and minimize uncertainties. Static parameters for 230 shallow rainfall-induced landslides in the continental United States are examined. ASTER GDEM is used as the basis for topographical characterization of slope and buffer analysis. Slope angle threshold assessment at the 50, 75, 95, 98, and 99 percentiles is tested locally. Further analysis of each threshold in relation to other parameters is investigated in a logistic regression model for the continental U.S. It is determined that lower than 95-percentile thresholds under-estimate slope angles and best regression fit can be achieved when utilizing the 99-threshold slope angle. This model predicts the highest number of cases correctly at 87.0% accuracy. A one-unit rise in the 99-threshold range increases landslide likelihood by 11.8%. The logistic regression model is carried over to ArcGIS where all static variables are processed based on their corresponding coefficients. A regional slope susceptibility map for the continental United States is developed and analyzed against the available landslide records and their spatial distributions. Consequently, a mathematical algorithm is proposed to determine landslide probability as a function of static and dynamic factors employing accumulated water volume. As rainfall thresholds alone do not provide information about the soil wetness profile with depth, the Shallow Landslide Index (SLI) is intended to be an indicator of antecedent root soil moisture and rainfall accumulation over a 1km2 pixel area. Experimentally, root-soil moisture retrieved from AMSR-E and rainfall retrieved from TRMM are used as proxies to develop such index. Static and dynamic conditions leading to each landslide event are examined over 60-days, 30-days, 10-days and 7-days. The input dataset is randomly divided into training and verification sets where validation results indicate that the best-fit model predicts the highest number of cases correctly at 93.2% accuracy. The resulting equation is then incorporated in a python subroutine that calculates the SLI for each of the 900,000-pixel points. For each pixel, the algorithm incrementally tries values from 0 to the value that makes the event probability equal to 1. Since AMSR-E and TRMM stopped working in October 2011 and April 2015 respectively, a solution that works for the future is presented. Root-soil moisture retrieved from SMAP and rainfall retrieved from GPM are used to develop models that calculate the SLI for the continental United States for 10-days, 7-days, and 3-days. The resulting models indicated a strong relationship (93.4%, 93.8%, and 93.7% respectively) between the predictors and the prediction value. Nevertheless, as of the writing of this work, the SMAP root soil moisture product has a mean latency of 7-days hence the SLI is functional for 10 or 7 days. It is expected that as SMAP’s latency is reduced, the SLI functionally can also be brought to a shorter period. The resulting SLI map can potentially be used as an indicator of the total amount of rainfall needed for a given duration of time to trigger a shallow landslide in a susceptible area

An Urban Flash Flood Alert Tool for Megacities—Application for Manhattan, New York City, USA

Urban flooding is a frequent problem affecting cities all over the world. The problem is more significant now that the climate is changing and urbanization trends are increasing. Various, physical hydrological models such as the Environmental Protection Agency Storm Water Management Model (EPA SWMM), MIKE URBAN-II and others, have been developed to simulate flooding events in cities. However, they require high accuracy mapping and a simulation of the underground storm drainage system. Sadly, this capability is usually not available for older or larger so-called megacities. Other hydrological model types are classified in the semi-physical category, like Cellular Automata (CA), require the incorporation of very fine resolution data. These types of data, in turn, demand massive computer power and time for analysis. Furthermore, available forecasting systems provide a way to determine total rainfall during extreme events, but they do not tell us what areas will be flooded. This work introduces an urban flooding tool that couples a rainfall-runoff model with a flood map database to expedite the alert process and estimate flooded areas. A 0.30-m Lidar Digital Elevation Model (DEM) of the study area (in this case Manhattan, New York City) is divided into 140 sub-basins. Several flood maps for each sub-basin are generated and organized into a database. For any forecasted extreme rainfall event, the rainfall-runoff model predicts the expected runoff volume at different times during the storm interval. The system rapidly searches for the corresponding flood map that delineates the expected flood area. The sensitivity analysis of parameters in the model show that the effect of storm inlet flow head is approximately linear while the effects of the threshold infiltration rate, the number of storm inlets, and the storm inlet flow reduction factor are non-linear. The reduction factor variation is found to exhibit a high non-linearity variation, hence requiring further detailed investigation

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has been hypothesized by local ecologists to result in the conversion of forest to grassland and subsequent increased fire danger. This hypothesis stands in contrast to empirical studies in the continental US which suggested that beetle mortality has only a negligible effect on fire danger. In response, we conducted a study using Landsat data and modeling techniques to map land cover change in the Kenai Peninsula and to integrate change maps with other geospatial data to predictively map fire danger for the same region. We collected Landsat imagery to map land cover change at roughly five-year intervals following a severe, mid-1990s beetle infestation to the present. Land cover classification was performed at each time step and used to quantify grassland encroachment patterns over time. The maps of land cover change along with digital elevation models (DEMs), temperature, and historical fire data were used to map and assess wildfire danger across the study area. Results indicate the highest wildfire danger tended to occur in herbaceous and black spruce land cover types, suggesting that the relationship between spruce beetle damage and wildfire danger in costal Alaskan forested ecosystems differs from the relationship between the two in the forests of the coterminous United States. These change detection analyses and fire danger predictions provide the Kenai National Wildlife Refuge (KENWR) ecologists and other forest managers a better understanding of the extent and magnitude of grassland conversion and subsequent change in fire danger following the 1990s spruce beetle outbreak

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has heightened local wildfire risk as canopy loss facilitates the conversion from bare to fire-prone grassland. We collected images from NASA satellite-based Earth observations to visualize land cover succession at roughly five-year intervals following a severe, mid-1990's beetle infestation to the present. We classified these data by vegetation cover type to quantify grassland encroachment patterns over time. Raster band math provided a change detection analysis on the land cover classifications. Results indicate the highest wildfire risk is linked to herbaceous and black spruce land cover types, The resulting land cover change image will give the Kenai National Wildlife Refuge (KENWR) ecologists a better understanding of where forests have converted to grassland since the 1990s. These classifications provided a foundation for us to integrate digital elevation models (DEMs), temperature, and historical fire data into a model using Python for assessing and mapping changes in wildfire risk. Spatial representations of this risk will contribute to a better understanding of ecological trajectories of beetle-affected landscapes, thereby informing management decisions at KENWR

Guidance Index for Shallow Landslide Hazard Analysis

Rainfall-induced shallow landslides are one of the most frequent hazards on slanted terrains. Intense storms with high-intensity and long-duration rainfall have high potential to trigger rapidly moving soil masses due to changes in pore water pressure and seepage forces. Nevertheless, regardless of the intensity and/or duration of the rainfall, shallow landslides are influenced by antecedent soil moisture conditions. As of this day, no system exists that dynamically interrelates these two factors on large scales. This work introduces a Shallow Landslide Index (SLI) as the first implementation of antecedent soil moisture conditions for the hazard analysis of shallow rainfall-induced landslides. The proposed mathematical algorithm is built using a logistic regression method that systematically learns from a comprehensive landslide inventory. Initially, root-soil moisture and rainfall measurements modeled from AMSR-E and TRMM respectively, are used as proxies to develop the index. The input dataset is randomly divided into training and verification sets using the Hold-Out method. Validation results indicate that the best-fit model predicts the highest number of cases correctly at 93.2% accuracy. Consecutively, as AMSR-E and TRMM stopped working in October 2011 and April 2015 respectively, root-soil moisture and rainfall measurements modeled by SMAP and GPM are used to develop models that calculate the SLI for 10, 7, and 3 days. The resulting models indicate a strong relationship (78.7%, 79.6%, and 76.8% respectively) between the predictors and the predicted value. The results also highlight important remaining challenges such as adequate information for algorithm functionality and satellite based data reliability. Nevertheless, the experimental system can potentially be used as a dynamic indicator of the total amount of antecedent moisture and rainfall (for a given duration of time) needed to trigger a shallow landslide in a susceptible area. It is indicated that the SLI algorithm can be re-built for other regions where deterministic studies are not feasible. This represents a significant step towards rainfall-induced shallow landslide hazard readiness

Assessing Rainfall Variability in Jamaica Using CHIRPS: Techniques and Measures for Persistence, Long and Short-Term Trends

Jamaica, as a Small Island Developing State (SIDS), is highly vulnerable to weather extremes. As precipitation persistence is a critical factor in determining the susceptibility of an area to risks, this work assesses the spatial and temporal variations of rainfall persistence in Jamaica from 1981 to 2020, using satellite-based information. The Hurst exponent (H) and the serial correlation coefficient (SCC) are used to evaluate the long-term persistence of precipitation and the Persistence Threshold (PT) concept is introduced to provide a description of rainfall characteristics over short periods, specifically, the number of consecutive days with precipitation above or below a set threshold value. The PT method is a novel concept that expands upon the Consecutive Dry Days (CDD) and Consecutive Wet Days (CWD) methods that only consider a threshold of 1 mm. Results show notable temporal and spatial variations in persistence over the decades, with an overall increasing trend in high precipitation persistence and a decreasing trend in low precipitation persistence. Geographically, the northern mountainous area of Jamaica received the most persistent rainfall over the study period with an observed increase in extreme rainfall events. The excess rainfall of the 2001–2010 decade is remarkable in this study, coinciding with the global unprecedented climate extremes during this time. We conclude that the data used in this study is viable for understanding and modeling rainfall trends in SIDS like Jamaica, and the derived PT method is a useful tool for short-term rainfall trends, but it is just one step toward determining flood or drought risk. Further research will focus on developing drought and flood indices

A Landslide Numerical Factor Derived from CHIRPS for Shallow Rainfall Triggered Landslides in Colombia

Despite great advances in remote sensing technologies, accurate satellite information is sometimes challenged in tropical regions where dense vegetation prevents the instruments from retrieving reliable readings. In this work, we introduce a satellite-based landslide rainfall threshold for the country of Colombia by studying 4 years of rainfall measurements from The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) for 346 rainfall-triggered landslide events (the dataset). We isolate the two successive rainy/dry periods leading to each landslide to create variables that simulate the dynamics of antecedent wetness and dryness. We test the performance of the derived variables (Rainfall Period 1 (PR1), Rainfall Sum 1 (RS1), Rainfall Period 2 (PR2), Rainfall Sum 2 (RS2), and Dry Period (DT)) in a logistic regression that includes three (3) static parameters (Soil Type (ST), Landcover (LC), and Slope angle). Results from the logistic model describe the influence of each variable in landslide occurrence with an accuracy of 73%. Subsequently, we use these dynamic variables to model a landslide threshold that, in the absence of satellite antecedent soil moisture data, helps describe the interactions between the dynamic variables and the slope angle. We name it the Landslide Triggering Factor—LTF. Subsequently, with a training dataset (65%) and one for testing (35%) we evaluate the LTF threshold performance and compare it to the well-known event duration (E-D) threshold. Results demonstrate that The LTF performs better than the E-D threshold for the training and testing datasets at 71% and 81% respectively

Guidance Index for Shallow Landslide Hazard Analysis

Rainfall-induced shallow landslides are one of the most frequent hazards on slanted terrains. Intense storms with high-intensity and long-duration rainfall have high potential to trigger rapidly moving soil masses due to changes in pore water pressure and seepage forces. Nevertheless, regardless of the intensity and/or duration of the rainfall, shallow landslides are influenced by antecedent soil moisture conditions. As of this day, no system exists that dynamically interrelates these two factors on large scales. This work introduces a Shallow Landslide Index (SLI) as the first implementation of antecedent soil moisture conditions for the hazard analysis of shallow rainfall-induced landslides. The proposed mathematical algorithm is built using a logistic regression method that systematically learns from a comprehensive landslide inventory. Initially, root-soil moisture and rainfall measurements modeled from AMSR-E and TRMM respectively, are used as proxies to develop the index. The input dataset is randomly divided into training and verification sets using the Hold-Out method. Validation results indicate that the best-fit model predicts the highest number of cases correctly at 93.2% accuracy. Consecutively, as AMSR-E and TRMM stopped working in October 2011 and April 2015 respectively, root-soil moisture and rainfall measurements modeled by SMAP and GPM are used to develop models that calculate the SLI for 10, 7, and 3 days. The resulting models indicate a strong relationship (78.7%, 79.6%, and 76.8% respectively) between the predictors and the predicted value. The results also highlight important remaining challenges such as adequate information for algorithm functionality and satellite based data reliability. Nevertheless, the experimental system can potentially be used as a dynamic indicator of the total amount of antecedent moisture and rainfall (for a given duration of time) needed to trigger a shallow landslide in a susceptible area. It is indicated that the SLI algorithm can be re-built for other regions where deterministic studies are not feasible. This represents a significant step towards rainfall-induced shallow landslide hazard readiness

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has been hypothesized by local ecologists to result in the conversion of forest to grassland and subsequent increased fire danger. This hypothesis stands in contrast to empirical studies in the continental US which suggested that beetle mortality has only a negligible effect on fire danger. In response, we conducted a study using Landsat data and modeling techniques to map land cover change in the Kenai Peninsula and to integrate change maps with other geospatial data to predictively map fire danger for the same region. We collected Landsat imagery to map land cover change at roughly five-year intervals following a severe, mid-1990s beetle infestation to the present. Land cover classification was performed at each time step and used to quantify grassland encroachment patterns over time. The maps of land cover change along with digital elevation models (DEMs), temperature, and historical fire data were used to map and assess wildfire danger across the study area. Results indicate the highest wildfire danger tended to occur in herbaceous and black spruce land cover types, suggesting that the relationship between spruce beetle damage and wildfire danger in costal Alaskan forested ecosystems differs from the relationship between the two in the forests of the coterminous United States. These change detection analyses and fire danger predictions provide the Kenai National Wildlife Refuge (KENWR) ecologists and other forest managers a better understanding of the extent and magnitude of grassland conversion and subsequent change in fire danger following the 1990s spruce beetle outbreak